1. Technical Field
One or more embodiments relate generally to systems and methods for image enhancement. More specifically, one or more embodiments of the present invention relate to systems and methods of modifying contrast in an image with visual artifact suppression.
2. Background and Relevant Art
Outdoor images and videos are often degraded by haze in the atmosphere. Due to atmospheric absorption and scattering, such images have lower contrast and visibility. In addition to the impact on visual quality, heavy haze can cause certain image processing tasks (e.g., stereo tracking and detection) during post-processing of an image to be more difficult than without the haze. Image processing typically involves modifying a contrast of an image to remove haze in the image prior to performing additional image processing tasks. Removing haze (or making any wide-scale adjustments to a contrast of an image), therefore, is often an important aspect of improving the efficiency and effectiveness of image post-processing operations, but is also a challenging and ill-posed problem.
Conventional contrast enhancement techniques generally perform poorly for dehazing images. Early techniques specific to dehazing an image focus on using multiple images for dehazing. Such early techniques use more than one image of the same scene (e.g., images with different degrees of polarization) to remove the haze. Although dehazing techniques that use multiple images can be useful when multiple images of the same scene are available in post processing, these techniques are not applicable when multiple images of the same scene are not available.
Other conventional contrast enhancement techniques rely on an assumption that input images have very high quality, as is the case of many natural hazy images. For low quality inputs that contain noise and artifacts, however, most existing methods will amplify the noise and artifacts (e.g., ringing, aliasing and blocky artifacts). For example, conventional single image dehazing techniques do not typically produce accurate results from low resolution or compressed images, such as images captured and processed by mobile devices or frames of highly compressed videos. In particular, while the conventional dehazing techniques may produce good results for high quality images, the same techniques often suffer from visual artifacts, including strong color shifting or amplification of existing artifacts for low quality input images.
To overcome the inaccuracies of conventional dehazing techniques in relation to low quality images, some conventional image processing techniques perform a pre-processing step to remove artifacts. Such artifacts are not easily removed from input images, however, and may remove image details that affect the dehazing process. On the other hand, attempting to remove enhanced or boosted artifacts produced by the dehazing process is often not effective unless the quality of the output image is sacrificed.
These and other disadvantages may exist with respect to conventional image dehazing techniques.